MPL.control: Control parameters for maximin projection learning
In ITRLearn: Statistical Learning for Individualized Treatment Regime
Description
Usage
Arguments
Value
See Also
Description
Parameters that control fitting of maximin projection learning.
Usage
1
MPL.control(pi.est = NULL, h.est = NULL, boot.sample = 600)
Arguments
pi.est
Estimated propentisy score for each patient. If not specified, a
logistic regression model is fitted to estimate the propensity score.
h.est
Estimated baseline function for each patient. If not specified, a linear
regression model is fitted to estimate the baseline function.
boot.sample
Number of bootstrap samples used for inference of the maximin effects
and the subgroup parameter. Default is 600.
Value
A list with the arguments specified.
See Also
ITRLearn documentation built on May 2, 2019, 11:03 a.m.
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Description Usage Arguments Value See Also
Description
Parameters that control fitting of maximin projection learning.
Usage
1 | MPL.control(pi.est = NULL, h.est = NULL, boot.sample = 600)
|
Arguments
pi.est |
Estimated propentisy score for each patient. If not specified, a logistic regression model is fitted to estimate the propensity score. |
h.est |
Estimated baseline function for each patient. If not specified, a linear regression model is fitted to estimate the baseline function. |
boot.sample |
Number of bootstrap samples used for inference of the maximin effects and the subgroup parameter. Default is 600. |
Value
A list with the arguments specified.
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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